Hive Introduction

Why Hive?

In big data projects, data is often stored in Hadoop Distributed File System (HDFS). The challenge is that analyzing this huge data using Hadoop’s default tool (MapReduce) requires writing complex Java code. Most business users and analysts are not comfortable with this approach.

That’s where Hive solves the problem:

  • Hive allows users to write queries in HiveQL (which looks like SQL) instead of writing MapReduce code.
  • It helps users perform tasks like filtering, grouping, joining, and summarizing data easily.
  • Hive automatically translates your queries into MapReduce jobs behind the scenes, so you don’t have to worry about the technical heavy lifting.

Example -

Without Hive → You write 100+ lines of Java code for a simple count.

With Hive → You write one line:

SELECT COUNT(*) FROM sales_data;

History?

The history of Hive began at Facebook. Around 2007-2008, Facebook engineers were facing a huge problem: they were collecting massive amounts of data every day and analyzing this data with MapReduce was becoming too slow, too complicated, and too technical.

To solve this, they built Hive—an easy-to-use system where data analysts could write SQL-like queries and run them on top of Hadoop’s distributed storage.

  • The goal was to make Hadoop accessible to non-programmers.
  • Hive was later made open source through the Apache Software Foundation.

Today, Hive is used by many companies like Netflix, Amazon, and Facebook to process petabytes of data.

What is Hive?

Apache Hive is a data warehouse software built on top of Hadoop. It helps you store, manage, and analyze big data using SQL-like queries. Instead of working directly with MapReduce, you can write simple HiveQL commands that Hive converts into complex processing tasks.

Key points:

  • It works with structured and semi-structured data.
  • Data is stored in HDFS, but you query it through Hive.
  • It uses a special query language called HiveQL (which is very similar to SQL).
  • Hive supports large-scale data summarization, ad-hoc queries, and reporting.

Example -

SELECT city, COUNT(*) 
FROM customer_data GROUP BY city;

With this simple query, Hive processes millions of records and gives you the result without you writing a single line of Java code.

In short, Hive = SQL for Hadoop.

Hive Advantages?

  • Easy to Learn: Hive’s HiveQL is similar to standard SQL. Anyone with basic SQL knowledge can quickly start writing queries.
  • Handles Big Data: Hive is built on top of Hadoop, so it can handle huge datasets stored across many machines. Even if your data is terabytes or petabytes, Hive can process it.
  • Scalability: Hive automatically distributes work across a cluster of computers. As your data grows, you can simply add more machines.
  • Extensibility: Hive supports custom functions (UDFs), so you can add your own logic if needed. You can also use Hive with Tez, Spark, or MapReduce as the execution engine.

Hive Disadvantages?

  • Slow for Real-Time Queries: Hive is designed for batch processing—not real-time queries. If you need quick, interactive results (like in online applications), Hive is too slow.
  • Latency: Queries in Hive usually have high latency because they generate MapReduce (or Spark/Tez) jobs behind the scenes. Even simple queries can take several minutes.
  • Limited Transactions: Hive is not a transactional database like MySQL or Oracle. It is not suitable for applications that need frequent updates or deletes.
  • No Row-Level Updates: Hive is best for read-heavy operations (data analysis, reporting). It does not support efficient row-by-row updates or deletes.

Hive Limitations?

  • Single row INSERT not supported.
  • There is no support for UPDATE or DELETE.
  • Limited number of built-in functions.
  • No data types for Date or Time.